AUTHOR=Peng Li-Hong, Yin Jun, Zhou Liqian, Liu Ming-Xi, Zhao Yan
TITLE=Human Microbe-Disease Association Prediction Based on Adaptive Boosting
JOURNAL=Frontiers in Microbiology
VOLUME=9
YEAR=2018
PAGES=2440
URL=https://www.frontiersin.org/article/10.3389/fmicb.2018.02440
DOI=10.3389/fmicb.2018.02440
ISSN=1664-302X
ABSTRACT=There are countless microbes in the human body, and they play various roles in the physiological process. There is growing evidence that microbes are closely associated with human diseases. Researching disease-related microbes helps us understand the mechanisms of diseases and provides new strategies for diseases diagnosis and treatment. Many computational models have been proposed to predict disease-related microbes, in this paper, we developed a model of Adaptive Boosting for Human Microbe-Disease Association prediction (ABHMDA) to reveal the associations between diseases and microbes by calculating the relation probability of disease-microbe pair using a strong classifier. Our model could be applied to new diseases without any known related microbes. In order to assess the prediction power of the model, global and local leave-one-out cross validation (LOOCV) were implemented. As shown in the results, the global and local LOOCV values reached 0.8869 and 0.7910, respectively. What’s more, 10, 10, and 8 out of the top 10 microbes predicted to be most likely to be associated with Asthma, Colorectal carcinoma and Type 1 diabetes were all verified by relevant literatures or database HMDAD, respectively. The above results verify the superior predictive performance of ABHMDA.